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  <div class="section" id="introduction">
<span id="intro"></span><h1>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">¶</a></h1>
<p>PyBrain&#8217;s concept is to encapsulate different data processing algorithms in what
we call a <tt class="xref docutils literal"><span class="pre">Module</span></tt>. A minimal Module contains a forward implementation
depending on a collection of free parameters that can be adjusted, usually
through some machine learning algorithm.</p>
<p>Modules have an input and an output buffer, plus corresponding error buffers
which are used in error backpropagation algorithms.</p>
<p>They are assembled into objects of the class <tt class="xref docutils literal"><span class="pre">Network</span></tt> and are
connected via <tt class="xref docutils literal"><span class="pre">Connection</span></tt> objects. These may contain a number of
adjustable parameters themselves, such as weights.</p>
<p>Note that a Network itself is again a Module, such that it is easy to build
hierarchical networks as well. Shortcuts exist for building the most common
network architectures, but in principle this system allows almost arbitrary
connectionist systems to be assembled, as long as they form a directed acyclic
graph.</p>  
<p>The free parameters of the Network are adjusted by means of a <tt class="xref docutils literal"><span class="pre">Trainer</span></tt>,
which uses a <tt class="xref docutils literal"><span class="pre">Dataset</span></tt> to learn the optimum parameters from examples.
For reinforcement learning experiments, a simulation environment with an
associated optimization task is used instead of a Dataset.</p>
<img alt="../_images/dataprocessing_flowchart.jpg" src="../_images/dataprocessing_flowchart.jpg" />
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